It has long been a big challenge in the field of automatic character recognition to recognize degraded machine-printed characters (character blurrings that may be caused by many reasons, such as low resolution character images, dithering of a digital camera, faxing or repeated scanning, etc.). Traditional methods usually use binary character images for dictionary generation, in which binarization means the value range of the pixels of an image can be only selected either as 0 and 255 or 0 and 1. However, for a degraded character image, binarization usually loses lots of useful information capable of effectively recognizing the information of that character for classification. With the loss of these lots of useful information, correct recognition is impossible even by man, due for example to binarization of the character ‘’ as ‘11’; that is to say, even if this ‘11’ is to be recognized by man, it will not be recognized as the result of the binarized character ‘’. Since character recognition is a mechanism mimicking man, a computer would never make correct recognition to the extent a man could not recognize, thereby causing grave consequences for subsequent recognition effect. In view of the fact that the value of the pixels of a grayscale character image is 0˜255, with the range of variation lying at 256, whereas a binarization image value is 0˜1, a grayscale character image can better retain the recognition information of a character, possess better representation of the image and contain more detailed information. Thus, it is essential in degraded character recognition to use grayscale images to generate a grayscale character dictionary. A grayscale dictionary is one directly constructed by grayscale character images. One problem of grayscale character dictionary generation is how to collect the character samples for dictionary making, since the number of character categories of oriental languages (Chinese, Japanese, and Korean) is very large. For example, a typical Japanese dictionary contains 4299 categories including Kanji, numerals, Katakana, Hiragana and symbols. Traditional dictionaries use binary character images obtained by a scanner. Collecting grayscale images can also be done via grayscale scanning by a scanner, but the grayscale character images required for making a grayscale character dictionary are much more than the binary character images required for making a binary character dictionary. Thus, manual collection is next to impossible.
Many methods have been so far proposed for degraded grayscale character image recognition, such as:    X. W. Wang, X. Q. Ding and C. S. Liu, “A gray-scale image based character recognition algorithm to low-quality and low-resolution images.” Proceedings of SPIE Vol. 4307, pp. 315-322.    Yoshimura, H., Etoh, M., Kondo, K., et al. “Gray-scale character recognition by gabor jets projection.” Proc. ICPR pp. 335-338, 2000.
Additionally, there is also patent related method of frequency-based feature extraction, such as U.S. Pat. No. 5,911,013, “Character recognition method and apparatus capable of handling handwriting”, submitted in Jun. 8, 1999 by the inventor Shinnosuke Taniishi.
However, for degraded grayscale character images, only frequency-based methods can not get very good result because they cannot effectively distinguish detailed features of a character, and are hence defective in recognizing similar characters.